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   "cell_type": "markdown",
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   "source": [
    "##### (1)利用tushare提取中国茅台数据中的最高最低价，作双折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>600519.SH</td>\n",
       "      <td>20190102</td>\n",
       "      <td>609.98</td>\n",
       "      <td>612.00</td>\n",
       "      <td>595.01</td>\n",
       "      <td>598.98</td>\n",
       "      <td>590.01</td>\n",
       "      <td>8.97</td>\n",
       "      <td>1.5203</td>\n",
       "      <td>62286.49</td>\n",
       "      <td>3754387.984</td>\n",
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       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>600519.SH</td>\n",
       "      <td>20190103</td>\n",
       "      <td>599.97</td>\n",
       "      <td>601.66</td>\n",
       "      <td>585.80</td>\n",
       "      <td>590.00</td>\n",
       "      <td>598.98</td>\n",
       "      <td>-8.98</td>\n",
       "      <td>-1.4992</td>\n",
       "      <td>30977.35</td>\n",
       "      <td>1838179.174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>600519.SH</td>\n",
       "      <td>20190104</td>\n",
       "      <td>587.28</td>\n",
       "      <td>607.67</td>\n",
       "      <td>582.02</td>\n",
       "      <td>602.00</td>\n",
       "      <td>590.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>2.0339</td>\n",
       "      <td>37683.47</td>\n",
       "      <td>2258501.484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>600519.SH</td>\n",
       "      <td>20190107</td>\n",
       "      <td>608.00</td>\n",
       "      <td>612.00</td>\n",
       "      <td>602.22</td>\n",
       "      <td>605.49</td>\n",
       "      <td>602.00</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.5797</td>\n",
       "      <td>34750.13</td>\n",
       "      <td>2105981.461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>600519.SH</td>\n",
       "      <td>20190108</td>\n",
       "      <td>605.50</td>\n",
       "      <td>612.00</td>\n",
       "      <td>600.28</td>\n",
       "      <td>604.79</td>\n",
       "      <td>605.49</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.1156</td>\n",
       "      <td>28838.13</td>\n",
       "      <td>1750413.219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       ts_code trade_date    open    high     low   close  pre_close  change  \\\n",
       "243  600519.SH   20190102  609.98  612.00  595.01  598.98     590.01    8.97   \n",
       "242  600519.SH   20190103  599.97  601.66  585.80  590.00     598.98   -8.98   \n",
       "241  600519.SH   20190104  587.28  607.67  582.02  602.00     590.00   12.00   \n",
       "240  600519.SH   20190107  608.00  612.00  602.22  605.49     602.00    3.49   \n",
       "239  600519.SH   20190108  605.50  612.00  600.28  604.79     605.49   -0.70   \n",
       "\n",
       "     pct_chg       vol       amount  \n",
       "243   1.5203  62286.49  3754387.984  \n",
       "242  -1.4992  30977.35  1838179.174  \n",
       "241   2.0339  37683.47  2258501.484  \n",
       "240   0.5797  34750.13  2105981.461  \n",
       "239  -0.1156  28838.13  1750413.219  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tushare as ts\n",
    "# 第一次使用需设置 token，在 https://waditu.com/ 注册并填写个人资料后即可获得权限\n",
    "token = '你的token值'\n",
    "tushare.set_token(token)\n",
    "# 获取行情数据\n",
    "df = ts.pro_bar(ts_code='600519.SH', start_date='20190101', end_date='20191231')\n",
    "df = df.sort_values(by=\"trade_date\", ascending=True)\n",
    "df.to_csv(\"中国茅台2019年数据.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "                \"20190121\",\n",
       "                \"20190122\",\n",
       "                \"20190123\",\n",
       "                \"20190124\",\n",
       "                \"20190125\",\n",
       "                \"20190128\",\n",
       "                \"20190129\",\n",
       "                \"20190130\",\n",
       "                \"20190131\",\n",
       "                \"20190201\",\n",
       "                \"20190211\",\n",
       "                \"20190212\",\n",
       "                \"20190213\",\n",
       "                \"20190214\",\n",
       "                \"20190215\",\n",
       "                \"20190218\",\n",
       "                \"20190219\",\n",
       "                \"20190220\",\n",
       "                \"20190221\",\n",
       "                \"20190222\",\n",
       "                \"20190225\",\n",
       "                \"20190226\",\n",
       "                \"20190227\",\n",
       "                \"20190228\",\n",
       "                \"20190301\",\n",
       "                \"20190304\",\n",
       "                \"20190305\",\n",
       "                \"20190306\",\n",
       "                \"20190307\",\n",
       "                \"20190308\",\n",
       "                \"20190311\",\n",
       "                \"20190312\",\n",
       "                \"20190313\",\n",
       "                \"20190314\",\n",
       "                \"20190315\",\n",
       "                \"20190318\",\n",
       "                \"20190319\",\n",
       "                \"20190320\",\n",
       "                \"20190321\",\n",
       "                \"20190322\",\n",
       "                \"20190325\",\n",
       "                \"20190326\",\n",
       "                \"20190327\",\n",
       "                \"20190328\",\n",
       "                \"20190329\",\n",
       "                \"20190401\",\n",
       "                \"20190402\",\n",
       "                \"20190403\",\n",
       "                \"20190404\",\n",
       "                \"20190408\",\n",
       "                \"20190409\",\n",
       "                \"20190410\",\n",
       "                \"20190411\",\n",
       "                \"20190412\",\n",
       "                \"20190415\",\n",
       "                \"20190416\",\n",
       "                \"20190417\",\n",
       "                \"20190418\",\n",
       "                \"20190419\",\n",
       "                \"20190422\",\n",
       "                \"20190423\",\n",
       "                \"20190424\",\n",
       "                \"20190425\",\n",
       "                \"20190426\",\n",
       "                \"20190429\",\n",
       "                \"20190430\",\n",
       "                \"20190506\",\n",
       "                \"20190507\",\n",
       "                \"20190508\",\n",
       "                \"20190509\",\n",
       "                \"20190510\",\n",
       "                \"20190513\",\n",
       "                \"20190514\",\n",
       "                \"20190515\",\n",
       "                \"20190516\",\n",
       "                \"20190517\",\n",
       "                \"20190520\",\n",
       "                \"20190521\",\n",
       "                \"20190522\",\n",
       "                \"20190523\",\n",
       "                \"20190524\",\n",
       "                \"20190527\",\n",
       "                \"20190528\",\n",
       "                \"20190529\",\n",
       "                \"20190530\",\n",
       "                \"20190531\",\n",
       "                \"20190603\",\n",
       "                \"20190604\",\n",
       "                \"20190605\",\n",
       "                \"20190606\",\n",
       "                \"20190610\",\n",
       "                \"20190611\",\n",
       "                \"20190612\",\n",
       "                \"20190613\",\n",
       "                \"20190614\",\n",
       "                \"20190617\",\n",
       "                \"20190618\",\n",
       "                \"20190619\",\n",
       "                \"20190620\",\n",
       "                \"20190621\",\n",
       "                \"20190624\",\n",
       "                \"20190625\",\n",
       "                \"20190626\",\n",
       "                \"20190627\",\n",
       "                \"20190628\",\n",
       "                \"20190701\",\n",
       "                \"20190702\",\n",
       "                \"20190703\",\n",
       "                \"20190704\",\n",
       "                \"20190705\",\n",
       "                \"20190708\",\n",
       "                \"20190709\",\n",
       "                \"20190710\",\n",
       "                \"20190711\",\n",
       "                \"20190712\",\n",
       "                \"20190715\",\n",
       "                \"20190716\",\n",
       "                \"20190717\",\n",
       "                \"20190718\",\n",
       "                \"20190719\",\n",
       "                \"20190722\",\n",
       "                \"20190723\",\n",
       "                \"20190724\",\n",
       "                \"20190725\",\n",
       "                \"20190726\",\n",
       "                \"20190729\",\n",
       "                \"20190730\",\n",
       "                \"20190731\",\n",
       "                \"20190801\",\n",
       "                \"20190802\",\n",
       "                \"20190805\",\n",
       "                \"20190806\",\n",
       "                \"20190807\",\n",
       "                \"20190808\",\n",
       "                \"20190809\",\n",
       "                \"20190812\",\n",
       "                \"20190813\",\n",
       "                \"20190814\",\n",
       "                \"20190815\",\n",
       "                \"20190816\",\n",
       "                \"20190819\",\n",
       "                \"20190820\",\n",
       "                \"20190821\",\n",
       "                \"20190822\",\n",
       "                \"20190823\",\n",
       "                \"20190826\",\n",
       "                \"20190827\",\n",
       "                \"20190828\",\n",
       "                \"20190829\",\n",
       "                \"20190830\",\n",
       "                \"20190902\",\n",
       "                \"20190903\",\n",
       "                \"20190904\",\n",
       "                \"20190905\",\n",
       "                \"20190906\",\n",
       "                \"20190909\",\n",
       "                \"20190910\",\n",
       "                \"20190911\",\n",
       "                \"20190912\",\n",
       "                \"20190916\",\n",
       "                \"20190917\",\n",
       "                \"20190918\",\n",
       "                \"20190919\",\n",
       "                \"20190920\",\n",
       "                \"20190923\",\n",
       "                \"20190924\",\n",
       "                \"20190925\",\n",
       "                \"20190926\",\n",
       "                \"20190927\",\n",
       "                \"20190930\",\n",
       "                \"20191008\",\n",
       "                \"20191009\",\n",
       "                \"20191010\",\n",
       "                \"20191011\",\n",
       "                \"20191014\",\n",
       "                \"20191015\",\n",
       "                \"20191016\",\n",
       "                \"20191017\",\n",
       "                \"20191018\",\n",
       "                \"20191021\",\n",
       "                \"20191022\",\n",
       "                \"20191023\",\n",
       "                \"20191024\",\n",
       "                \"20191025\",\n",
       "                \"20191028\",\n",
       "                \"20191029\",\n",
       "                \"20191030\",\n",
       "                \"20191031\",\n",
       "                \"20191101\",\n",
       "                \"20191104\",\n",
       "                \"20191105\",\n",
       "                \"20191106\",\n",
       "                \"20191107\",\n",
       "                \"20191108\",\n",
       "                \"20191111\",\n",
       "                \"20191112\",\n",
       "                \"20191113\",\n",
       "                \"20191114\",\n",
       "                \"20191115\",\n",
       "                \"20191118\",\n",
       "                \"20191119\",\n",
       "                \"20191120\",\n",
       "                \"20191121\",\n",
       "                \"20191122\",\n",
       "                \"20191125\",\n",
       "                \"20191126\",\n",
       "                \"20191127\",\n",
       "                \"20191128\",\n",
       "                \"20191129\",\n",
       "                \"20191202\",\n",
       "                \"20191203\",\n",
       "                \"20191204\",\n",
       "                \"20191205\",\n",
       "                \"20191206\",\n",
       "                \"20191209\",\n",
       "                \"20191210\",\n",
       "                \"20191211\",\n",
       "                \"20191212\",\n",
       "                \"20191213\",\n",
       "                \"20191216\",\n",
       "                \"20191217\",\n",
       "                \"20191218\",\n",
       "                \"20191219\",\n",
       "                \"20191220\",\n",
       "                \"20191223\",\n",
       "                \"20191224\",\n",
       "                \"20191225\",\n",
       "                \"20191226\",\n",
       "                \"20191227\",\n",
       "                \"20191230\",\n",
       "                \"20191231\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"type\": \"value\",\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"min\": 400,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_72e34f6b05044fd8bb13d591015fc6cc.setOption(option_72e34f6b05044fd8bb13d591015fc6cc);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x7fc444e46828>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Line\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 将时间提取出来并转成list\n",
    "Time = df.trade_date.values.tolist()\n",
    "# 将最高价格提取出来并转成list\n",
    "high_price = df.high.values.tolist()\n",
    "# 将每日最低价格提取出来并转成list\n",
    "low_price = df.low.values.tolist()\n",
    "\n",
    "# 构造折线图对象\n",
    "line = Line()\n",
    "# 添加横坐标数值\n",
    "line.add_xaxis(Time)\n",
    "# 添加纵坐标数值\n",
    "line.add_yaxis(\"high\",high_price)\n",
    "line.add_yaxis(\"low\",low_price)\n",
    "# 设置标题为折线图\n",
    "line.set_global_opts(title_opts = opts.TitleOpts(title = \"折线图\"))\n",
    "# 设置纵坐标最小值为400\n",
    "line.set_global_opts(yaxis_opts=opts.AxisOpts(type_=\"value\",min_=400,))\n",
    "\n",
    "# 渲染图片\n",
    "line.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (2)计算中国茅台的平均涨跌幅 pct_chg，并分别以水球图和仪表盘图表现出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min', 'echarts-liquidfill':'https://assets.pyecharts.org/assets/echarts-liquidfill.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"345e5b564a544dbebb6ef281000a07a4\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', 'echarts-liquidfill'], function(echarts) {\n",
       "                var chart_345e5b564a544dbebb6ef281000a07a4 = echarts.init(\n",
       "                    document.getElementById('345e5b564a544dbebb6ef281000a07a4'), 'white', {renderer: 'canvas'});\n",
       "                var option_345e5b564a544dbebb6ef281000a07a4 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"liquidFill\",\n",
       "            \"data\": [\n",
       "                0.3096659836065574\n",
       "            ],\n",
       "            \"waveAnimation\": true,\n",
       "            \"animationDuration\": 2000,\n",
       "            \"animationDurationUpdate\": 1000,\n",
       "            \"color\": [\n",
       "                \"#294D99\",\n",
       "                \"#156ACF\",\n",
       "                \"#1598ED\",\n",
       "                \"#45BDFF\"\n",
       "            ],\n",
       "            \"shape\": \"circle\",\n",
       "            \"backgroundStyle\": {},\n",
       "            \"outline\": {\n",
       "                \"show\": true,\n",
       "                \"borderDistance\": 8\n",
       "            },\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8,\n",
       "                \"fontSize\": 50\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u4e2d\\u56fd\\u8305\\u53f02019\\u5e73\\u5747\\u65e5\\u6da8\\u5e45\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_345e5b564a544dbebb6ef281000a07a4.setOption(option_345e5b564a544dbebb6ef281000a07a4);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x7fc446be95f8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入pyecharts.charts中的Bar，作水球图\n",
    "from pyecharts.charts import Liquid, Page\n",
    "# 导入配置项入口\n",
    "from pyecharts import options as opts\n",
    "\n",
    "from pyecharts.globals import SymbolType\n",
    "\n",
    "# 中国茅台的平均日涨幅\n",
    "pct_chg = df[\"pct_chg\"].mean()\n",
    "\n",
    "# 构造水球图对象\n",
    "l = Liquid()\n",
    "# l.add(\"\", [显示百分比(可以有多组数据)])\n",
    "l.add(\"\", [pct_chg])\n",
    "# 设置标题\n",
    "l.set_global_opts(title_opts=opts.TitleOpts(title=\"中国茅台2019平均日涨幅\"))\n",
    "\n",
    "# 渲染图片\n",
    "l.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"aff5ce748c7c4d6ba506aa68c94911d3\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_aff5ce748c7c4d6ba506aa68c94911d3 = echarts.init(\n",
       "                    document.getElementById('aff5ce748c7c4d6ba506aa68c94911d3'), 'white', {renderer: 'canvas'});\n",
       "                var option_aff5ce748c7c4d6ba506aa68c94911d3 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"gauge\",\n",
       "            \"title\": {\n",
       "                \"show\": true,\n",
       "                \"offsetCenter\": [\n",
       "                    0,\n",
       "                    \"-40%\"\n",
       "                ],\n",
       "                \"color\": \"#333\",\n",
       "                \"fontStyle\": \"normal\",\n",
       "                \"fontWeight\": \"normal\",\n",
       "                \"fontFamily\": \"sans-serif\",\n",
       "                \"fontSize\": 15,\n",
       "                \"backgroundColor\": \"transparent\",\n",
       "                \"borderColor\": \"transparent\",\n",
       "                \"borderWidth\": 0,\n",
       "                \"borderRadius\": 0,\n",
       "                \"padding\": 0,\n",
       "                \"shadowColor\": \"transparent\",\n",
       "                \"shadowBlur\": 0,\n",
       "                \"shadowOffsetX\": 0,\n",
       "                \"shadowOffsetY\": 0\n",
       "            },\n",
       "            \"detail\": {\n",
       "                \"show\": true,\n",
       "                \"backgroundColor\": \"transparent\",\n",
       "                \"borderWidth\": 0,\n",
       "                \"borderColor\": \"transparent\",\n",
       "                \"offsetCenter\": [\n",
       "                    0,\n",
       "                    \"-40%\"\n",
       "                ],\n",
       "                \"formatter\": \"{value}%\",\n",
       "                \"color\": \"auto\",\n",
       "                \"fontStyle\": \"normal\",\n",
       "                \"fontWeight\": \"normal\",\n",
       "                \"fontFamily\": \"sans-serif\",\n",
       "                \"fontSize\": 15,\n",
       "                \"borderRadius\": 0,\n",
       "                \"padding\": 0,\n",
       "                \"shadowColor\": \"transparent\",\n",
       "                \"shadowBlur\": 0,\n",
       "                \"shadowOffsetX\": 0,\n",
       "                \"shadowOffsetY\": 0\n",
       "            },\n",
       "            \"name\": \"\\u4e2d\\u56fd\\u8305\\u53f02019\\u5e73\\u5747\\u65e5\\u6da8\\u5e45\",\n",
       "            \"min\": 0,\n",
       "            \"max\": 100,\n",
       "            \"splitNumber\": 10,\n",
       "            \"radius\": \"75%\",\n",
       "            \"startAngle\": 225,\n",
       "            \"endAngle\": -45,\n",
       "            \"clockwise\": true,\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"value\": 30.97\n",
       "                }\n",
       "            ],\n",
       "            \"pointer\": {\n",
       "                \"show\": true,\n",
       "                \"length\": \"80%\",\n",
       "                \"width\": 8\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u4e2d\\u56fd\\u8305\\u53f02019\\u5e73\\u5747\\u65e5\\u6da8\\u5e45\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u4e2d\\u56fd\\u8305\\u53f02019\\u5e73\\u5747\\u65e5\\u6da8\\u5e45\": true\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    }\n",
       "};\n",
       "                chart_aff5ce748c7c4d6ba506aa68c94911d3.setOption(option_aff5ce748c7c4d6ba506aa68c94911d3);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x7fc444e469b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.charts import Page,Gauge\n",
    "from pyecharts import options as opts\n",
    "\n",
    "g = Gauge()\n",
    "g.add(\"中国茅台2019平均日涨幅\",[(\"\", round(pct_chg * 100, 2))])\n",
    "# g.set_global_opts(title_opts = opts.TitleOpts(title=\"中国茅台2019平均日涨幅\"))\n",
    "g.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （3）统计A股各行业公司数量，并跟进行业分布数据作词云图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>symbol</th>\n",
       "      <th>name</th>\n",
       "      <th>area</th>\n",
       "      <th>industry</th>\n",
       "      <th>market</th>\n",
       "      <th>list_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001</td>\n",
       "      <td>平安银行</td>\n",
       "      <td>深圳</td>\n",
       "      <td>银行</td>\n",
       "      <td>主板</td>\n",
       "      <td>19910403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002</td>\n",
       "      <td>万科A</td>\n",
       "      <td>深圳</td>\n",
       "      <td>全国地产</td>\n",
       "      <td>主板</td>\n",
       "      <td>19910129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004</td>\n",
       "      <td>国农科技</td>\n",
       "      <td>深圳</td>\n",
       "      <td>互联网</td>\n",
       "      <td>主板</td>\n",
       "      <td>19910114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000005.SZ</td>\n",
       "      <td>000005</td>\n",
       "      <td>世纪星源</td>\n",
       "      <td>深圳</td>\n",
       "      <td>环境保护</td>\n",
       "      <td>主板</td>\n",
       "      <td>19901210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000006.SZ</td>\n",
       "      <td>000006</td>\n",
       "      <td>深振业A</td>\n",
       "      <td>深圳</td>\n",
       "      <td>区域地产</td>\n",
       "      <td>主板</td>\n",
       "      <td>19920427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>000007.SZ</td>\n",
       "      <td>000007</td>\n",
       "      <td>全新好</td>\n",
       "      <td>深圳</td>\n",
       "      <td>酒店餐饮</td>\n",
       "      <td>主板</td>\n",
       "      <td>19920413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>000008.SZ</td>\n",
       "      <td>000008</td>\n",
       "      <td>神州高铁</td>\n",
       "      <td>北京</td>\n",
       "      <td>运输设备</td>\n",
       "      <td>主板</td>\n",
       "      <td>19920507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>000009.SZ</td>\n",
       "      <td>000009</td>\n",
       "      <td>中国宝安</td>\n",
       "      <td>深圳</td>\n",
       "      <td>综合类</td>\n",
       "      <td>主板</td>\n",
       "      <td>19910625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>000010.SZ</td>\n",
       "      <td>000010</td>\n",
       "      <td>美丽生态</td>\n",
       "      <td>深圳</td>\n",
       "      <td>建筑工程</td>\n",
       "      <td>主板</td>\n",
       "      <td>19951027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011</td>\n",
       "      <td>深物业A</td>\n",
       "      <td>深圳</td>\n",
       "      <td>区域地产</td>\n",
       "      <td>主板</td>\n",
       "      <td>19920330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ts_code  symbol  name area industry market list_date\n",
       "0  000001.SZ  000001  平安银行   深圳       银行     主板  19910403\n",
       "1  000002.SZ  000002   万科A   深圳     全国地产     主板  19910129\n",
       "2  000004.SZ  000004  国农科技   深圳      互联网     主板  19910114\n",
       "3  000005.SZ  000005  世纪星源   深圳     环境保护     主板  19901210\n",
       "4  000006.SZ  000006  深振业A   深圳     区域地产     主板  19920427\n",
       "5  000007.SZ  000007   全新好   深圳     酒店餐饮     主板  19920413\n",
       "6  000008.SZ  000008  神州高铁   北京     运输设备     主板  19920507\n",
       "7  000009.SZ  000009  中国宝安   深圳      综合类     主板  19910625\n",
       "8  000010.SZ  000010  美丽生态   深圳     建筑工程     主板  19951027\n",
       "9  000011.SZ  000011  深物业A   深圳     区域地产     主板  19920330"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入tushare包，需要pip安装\n",
    "import tushare as ts\n",
    "# 获取股票基本数据\n",
    "pro = ts.pro_api()\n",
    "df = pro.stock_basic()\n",
    "# 展示前10行数据\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>公司数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IT设备</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>专用机械</th>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中成药</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乳制品</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>互联网</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>银行</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陶瓷</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>食品</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>饲料</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黄金</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          公司数量\n",
       "industry      \n",
       "IT设备        38\n",
       "专用机械       159\n",
       "中成药         71\n",
       "乳制品         14\n",
       "互联网         81\n",
       "...        ...\n",
       "银行          37\n",
       "陶瓷           7\n",
       "食品          68\n",
       "饲料          17\n",
       "黄金          13\n",
       "\n",
       "[110 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 利用groupby函数，以【industry】字段进行groupby，同时结合agg聚合函数统计各个行业的公司数量，将统计所得数据命名为【公司数量】\n",
    "# df.groupby(\"被统计的列\")[\"选择一列做运算\"].agg({\"新列名\": \"count\"})\n",
    "df_industry = df.groupby(\"industry\")[\"industry\"].agg(公司数量=(\"count\"))\n",
    "df_industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('IT设备', 38), ('专用机械', 159), ('中成药', 71), ('乳制品', 14), ('互联网', 81), ('仓储物流', 40), ('供气供热', 30), ('保险', 7), ('元器件', 201), ('全国地产', 35), ('公共交通', 8), ('公路', 7), ('其他商业', 14), ('其他建材', 27), ('农业综合', 32), ('农用机械', 9), ('农药化肥', 42), ('出版业', 22), ('化学制药', 117), ('化工原料', 186), ('化工机械', 10), ('化纤', 29), ('区域地产', 60), ('医疗保健', 102), ('医药商业', 26), ('半导体', 69), ('商品城', 3), ('商贸代理', 23), ('啤酒', 7), ('园区开发', 16), ('塑料', 53), ('多元金融', 28), ('家居用品', 43), ('家用电器', 45), ('小金属', 35), ('工程机械', 29), ('广告包装', 42), ('建筑工程', 91), ('影视音像', 46), ('房产服务', 11), ('批发业', 7), ('摩托车', 7), ('文教休闲', 37), ('新型电力', 15), ('旅游景点', 12), ('旅游服务', 13), ('日用化工', 11), ('普钢', 25), ('服饰', 58), ('机场', 4), ('机床制造', 16), ('机械基件', 80), ('林业', 4), ('染料涂料', 23), ('橡胶', 12), ('水力发电', 20), ('水务', 11), ('水泥', 22), ('水运', 11), ('汽车整车', 23), ('汽车服务', 12), ('汽车配件', 154), ('渔业', 8), ('港口', 19), ('火力发电', 33), ('焦炭加工', 7), ('煤炭开采', 28), ('特种钢', 10), ('环境保护', 90), ('玻璃', 20), ('生物制药', 50), ('电信运营', 9), ('电器仪表', 73), ('电器连锁', 2), ('电气设备', 192), ('白酒', 18), ('百货', 42), ('石油加工', 9), ('石油开采', 19), ('石油贸易', 7), ('矿物制品', 22), ('种植业', 14), ('空运', 8), ('红黄酒', 10), ('纺织', 40), ('纺织机械', 11), ('综合类', 35), ('航空', 28), ('船舶', 7), ('装修装饰', 29), ('证券', 48), ('超市连锁', 12), ('路桥', 19), ('软件服务', 221), ('软饮料', 8), ('轻工机械', 10), ('运输设备', 25), ('通信设备', 127), ('造纸', 27), ('酒店餐饮', 10), ('钢加工', 27), ('铁路', 5), ('铅锌', 13), ('铜', 14), ('铝', 29), ('银行', 37), ('陶瓷', 7), ('食品', 68), ('饲料', 17), ('黄金', 13)]\n"
     ]
    },
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       "    \"tooltip\": {\n",
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       "        \"trigger\": \"item\",\n",
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       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"WordCloud-\\u57fa\\u672c\\u793a\\u4f8b\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_14b834c363204b5a8378433633edb6ed.setOption(option_14b834c363204b5a8378433633edb6ed);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x7fc4468aeb70>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Page, WordCloud\n",
    "from pyecharts.globals import SymbolType\n",
    "\n",
    "# 提取各个行业以及其所对应的公司数量，即 行业名字：公司数量\n",
    "words = list(zip(df_industry.index.values.tolist(), df_industry[\"公司数量\"].values.tolist()))\n",
    "print(words)\n",
    "\n",
    "# 构造词云图对象\n",
    "wc = WordCloud()\n",
    "# wc.add(\"\", 数据, word_size_range=[词云图文字的大小范围])\n",
    "wc.add(\"\", words, word_size_range=[20, 100])\n",
    "# 设置标题\n",
    "wc.set_global_opts(title_opts=opts.TitleOpts(title=\"WordCloud-基本示例\"))\n",
    "# 渲染图片\n",
    "wc.render_notebook()"
   ]
  }
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